Accurate wavelength measurement is critical for spectroscopy,optical communications,semiconductor manufacturing,and quantum research.Emerging reconstructive wavemeters are compact,cost-effective devices that utilize p...Accurate wavelength measurement is critical for spectroscopy,optical communications,semiconductor manufacturing,and quantum research.Emerging reconstructive wavemeters are compact,cost-effective devices that utilize pseudo-random wavelength patterns and computational techniques to provide high-resolution,broadband alternatives to solutions based on frequency beating and interferometry.We propose a novel reconstructive wavemeter that synergizes the advantages of both approaches.Its physical model is based on the integration of thousands of high-quality-factor optical microcavities,which are deformed to stimulate whispering gallery mode splitting.For realizing a wavelength interpreter,we developed a hybrid machine learning approach utilizing boosting methods and variational autoencoders.This enabled the implementation of wavelength interpretation as a rigorous regression task for the first time.The introduced novel concept ensures the uniqueness of the wavelength patterns up to ultra-wide(~100 nm)spectral window while guarantees high(~100 fm)intrinsic sensitivity.The latter allocates the proposed solution right next to the ultimate reconstructive wavemeters based on integrating spheres,but with less calibration efforts,featuring superior miniaturization options and chip-scale integrability.展开更多
文摘Accurate wavelength measurement is critical for spectroscopy,optical communications,semiconductor manufacturing,and quantum research.Emerging reconstructive wavemeters are compact,cost-effective devices that utilize pseudo-random wavelength patterns and computational techniques to provide high-resolution,broadband alternatives to solutions based on frequency beating and interferometry.We propose a novel reconstructive wavemeter that synergizes the advantages of both approaches.Its physical model is based on the integration of thousands of high-quality-factor optical microcavities,which are deformed to stimulate whispering gallery mode splitting.For realizing a wavelength interpreter,we developed a hybrid machine learning approach utilizing boosting methods and variational autoencoders.This enabled the implementation of wavelength interpretation as a rigorous regression task for the first time.The introduced novel concept ensures the uniqueness of the wavelength patterns up to ultra-wide(~100 nm)spectral window while guarantees high(~100 fm)intrinsic sensitivity.The latter allocates the proposed solution right next to the ultimate reconstructive wavemeters based on integrating spheres,but with less calibration efforts,featuring superior miniaturization options and chip-scale integrability.
